Using scientifically credible data to underpin connected health

Transcription

1 Using scientifically credible data to underpin connected health Siân Astley, EuroFIR AISBL, Belgium On behalf of the QuaLify consortium Framework Programme for research, This technological project has development received funding and from demonstration the European under Union s grant Seventh agreement no

2 Quantify Life Feed Yourself Countries: 10 Budget: 2.5 Mio EU Funding: 2 Mio EU Duration: 2 years Coordination: Stephen Webb, RTDS (AT) Scientific Coordinator: Ben van Ommen, TNO (NL)

3 QuaLiFY: Aims and objectives Exploit data and results from previous EU-funded projects Optimise opportunities for commercial personalised dietary advice Integrate complementary services for commercial activities Identify and scientifically assess best practice methods and technologies in personalised dietary advice services and integrate tailored solutions for target group(s) (WP1) Provide access to knowledge databases and technologies for professional and scientifically solid personalised dietary advice services (WP2) Provide a proof-of-principle ICT infrastructure that allows personal data handling (WP2) Validate the functionalities of these solutions in a number of real-life environments (WP3), namely young people who are obese/ over-weight (free living) or diagnosed with eating disorder (clinic-based), and Type 2 (pre-)diabetics (dietitians) Construct an open innovation business model for SME commercialisation (WP4)

4 Failure: Individual cost Half of all men and women are overweight across the EU 200 million adults More men are overweight, more women are obese UK has most overweight (70% of men, 60% of women, ¼ are obese) 20% of men and 23% of women are obese (OB) Ireland has the highest prevalence of obese women Majority of MS have 20% or higher obesity Nearly a quarter (24%) of children aged 5-17 years are over-weight or obese More people aged y are overweight or obese than y Older individuals are already at increased risk of other morbidities (e.g. CVD) Globally, ca. 8% of men and 10% of women are obese

5 Cost to nations 25% higher health expenditure in overweight and obese Obesity is responsible for 1-8% of total health expenditure UK National Health Service 6.1 bn per annum (total 131 bn, 4.5%) Cost to wider UK economy 22 bn EU-wide expenditure 81 bn per annum (wider economy 1. 5 trillion) Greek national debt in 2012 ca. 304 bn

6 Quantify Life Feed Yourself

7 Adult female (19-64 years of age) Ca Kcal. 76g fat < 24g saturated 260g carbohydrate < 57g added sugar 18g fibre 50g protein Salt < 6g equivalent Vitamins, minerals, and all those things Water (~ 2 litres or 4 pints) At least 150 minutes of moderate aerobic activity weekly (e.g. cycling or fast walking) Strength exercises on two or more days a week that work all the major muscles OR 75 minutes of vigorous aerobic activity weekly (e.g. running or tennis) Strength exercises on two or more days OR A mix of moderate and vigorous aerobic activity every week, e.g. two 30-minute runs plus 30 minutes of fast walking and strength exercises on two or more days

8 Traffic lights and GDAs 1. Base your meals on starchy foods wholegrain bread, pasta and rice 2. Eat lots of fruit and vegetables At least 5 portions (of ca. 80g) 3. Eat more fish 4. Cut down on saturated fat and sugar 5. Try to eat less salt - no more than 6g a day 6. Get active and try to be a healthy weight 7. Drink plenty of water 8. Don't skip breakfast

9 One size does not fit all

10 Quantify Life Feed Yourself Measure, interpret and communicate

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13 Collecting data User information (sex, age, weight, measurements) User goals (e.g. loose weight, eat less saturated fat, do more exercise) User behaviours (e.g. food eaten, exercise completed) Food composition data sourced from published and brand information Added to by user community Personalised database (frequently eaten foods) Bar scanner Recipe calculator Variety of units (grams, ounces, cups, packets, 100 g, 1 g, 1 unit) Outputs (e.g. energy used [e.g. steps, exercise], calories consumed, progress)

14 Personalised health apps mhealth apps projected market value ca. 6 bn 60% from remote monitoring Potential predicted saving in healthcare ca. 99 bn 69 bn wellness/ prevention, 32 bn treatment and monitoring Costs (e.g. workforce) ca. 6 bn Top 20 sports/ fitness/ health apps in 2013 = 231 million installations 2017, 3.4 bn smartphones, ca. 50% using mhealth apps mhealth apps currently available 70% targeted at consumers, 30% at professionals (e.g. dietitians) Two-thirds do not act on advice Nearly half (45%) are concerned about security

15 Not acting on advice Because we don t but there is evidence apps are more effective Persist for longer, more accurate, novel, appeal to younger age groups Engaging adolescents in healthy lifestyle choices: Paper diary versus App Improve health based on personalised advice and new technology Food diaries (paper) versus FoodWiz App Food intake and exercise in adolescents (16-19 years old) Ease of use, acceptability and perceived effectiveness Florence Jimoh & James Lay (Food Angels Ltd.)

16 Electronic diary (app & platform) Scan barcodes Add own product Record moods Weigh food via Bluetooth scale Set targets Monitoring Nudging

17 Study Protocol: Methods Recruitment (5 schools, 34 participants, 30 completed) Nutrition education session Fortnightly meetings (BMI, WC, Review diet) Support messages Paper diary received general support App received general support and targets messages (diet and activity) Followed up with a questionnaire Paper-based food diaries and exercise record App for food intake and exercise record 4-weeks intervention break 4-weeks intervention

18 App easier to use Percentage agreement with statements (%) Paper diary App 0.0 Time consuming Boring Enjoyable Convenient Would recommend to a friend Overall liked it Would use again in thefuture. Negative: Diary scores higher Positive: App scores higher

19 Comfortable with App Percentage agreement with statements (%) Paper diary App 0.0 Comfortable using in front of friends Comfortable using when with family Comfortable using at school Comfortable using in social settings

20 Perceived impact on intake % agreement with statements Changed Snacks portion size Swapped less healthy foods for more healthy ones Ate more fruits and vegetables Ate less saturated fat Ate more fibre Ate less sugar Paper diary App BMI Start Finish Normal Overweight 8 6 Obese 3 3

21 Perceived impact on activity % agreement with statements Paper diary App 0.0 Did more aerobic exercises Did more strength exercises Tried new activities

22 Summary FoodWiz2 App Likes: Convenient Diet awareness Calorie count Barcode scan Weigh via Bluetooth Dislikes Limited exercise options Couldn t find some foods Sometimes slow Paper Diary Likes: Simple Diet awareness Exercise awareness Dislikes Time consuming Weighing the food

23 Summary Recording of food + exercise à more aware of their health General preference for app Useful for young people to monitor intake, activity and mood Relevant for other age groups familiar with or willing to try technology

24 Not acting on advice Apparently inaccurate or conflicting information e.g. Speedo app 64 x 25 m lengths (10 FC, 10 BS, 44 BrS) 784 kcal MyfitnessPal 1300 kcal Device Day 1 (steps) Day 2 (steps) Pedometer (10% ) Jawbone Up (8% ) Basis band (25% ) Fitbit Flex (7% ) Nike (1% ) hhp://bit.ly/1qq1bry

25 Validity and context of output This food is low in saturated fat or This food is high in saturated fat This food has lots of vitamin C Skittles TM as well as oranges This food is high in protein Ø Most consider a snap shot rather than longer term behaviours Ø Few suggest changes or provide advice 1. Alternative foods 2. Foods high in missing/ low intakes 3. Alert users to low intakes only high

26 Validated data and knowledge

27 Delivering data and knowledge Data and knowledge applicable to personalised dietary advice Intake (e.g. food composition and consumption) Status (blood chemistry, genotype) Health markers (RDI, optimal blood range, phenotype, etc.) Quisper manages access, application, advice outputs Tools retrieve information via application programming interface (API) Combine data and knowledge to give an output Customers (tools) control delivery format End-users (customers) offered assurance about validity and security

28 Validated data and knowledge Peer-reviewed based on harmonised or best-available standards e.g. Food composition, CEN EN 16104:2012 Food data Structure and interchange format, published 19 th December 2012 Expert researchers trained to critically assess data or knowledge from peerreviewed and/ or grey literature based on: Source (e.g. bibliographic reference), information (e.g. study design, power, etc.), method of analysis (e.g. specificity, standards, etc.), sampling information (e.g. number and frequency, replicates), and analytical data (e.g. certainty, errors, etc.) e.g. Was accuracy determined using certified or secondary reference materials? Aspects weighted for value (e.g. study design > method >> source) Scored on the basis of quality Panel (ca. 50) made of sub-panels (e.g. food composition, 10) requiring only 4-5 to review each set of data or knowledge rules Each sub-panel asked to consider standards as fit-for-purpose annually

29 Personalised dietary advice VitalinQ is an Dutch SME partner in QuaLiFY Supporting behaviour change, i.e. healthier lifestyle long-term Tailored to individuals preferences with choices in nutrition (balanced diet), exercise (age-appropriate), planning, work, news, recipes and environment No separate App required for those with chronic conditions (e.g. diabetes) Supported by a team of dieticians, lifestyle coaches, physiotherapists and other allied healthcare professions; mindful eating and choices Multiple module interface, direct feedback More than 1 million Dutch and Belgian users Users can choose to be part of the wider (specified) community Launching a version in English Making more use of images and visual recognition

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32 Personalised dietary advice Safecape Software Solutions is an Greek SME partner in QuaLiFY Silver ISV Microsoft Partner and recognized ISV, developing high technology information systems and innovative software applications Insurance, accountancy, banking and personalised healthcare Genotyping, 45 SNPs as well as phenotype and physical activity Outputs are personalised recommendations and goals based genes, diet, lifestyle and other biomarkers Increase or decrease intake of nutrient or specific food Predisposition to conditions such celiac disease, lactose intolerance Reports available in a variety of formats (e.g. MS Word, PDF, HTML etc.) or as machine-readable that can be integrated with other information Inherently multilingual and both input and output can be provided in any language Willing to work with out service providers to deliver end-user information

33 Safecape: Logic process

34 Conclusion mhealth is valuable market with space for many suppliers Apps are being provided by experts in technology not diet and health Diet and health researchers and allied healthcare providers need to get involved and ensure end-users have confidence in outputs Not all end-users will be successful, but the current approaches are not working either; more tools more successes (as well as failures) Scientifically validated data and knowledge will help confidence in both resources and end-users chances of success Enabling end-users and providing relevant information

35 Thank you for your attention! For more information find us at Contacts: Presenter: Siân Astley (EuroFIR AISBL),